Take control of your home video, television and music, with a media centre build based on Raspberry Pi. Your film collection deserves the best!
Trees are brilliant! They capture carbon, they keep urban streets cool, their roots slow down erosion, they provide habitat for millions of other life forms, and loads more. Meet the intelligent garden system that’s using Raspberry Pi and AI to monitor the health of trees, bees and other garden visitors.
Take a Raspberry Pi Pico 2 W, a couple of H-bridges, motors and a servo, and you too can build a remote-controlled car. Many have done it, but to our knowledge only Eugene Tkachenko has 3D printed everything else, from the wheels to the chassis through to the mechanical parts such as the drive train and gears. It’s a beautiful bit of work.
If you like to take your music out and about, and you yearn for the days when we used to respect proper album artwork, you’ll like the PiPod, a mobile MP3 player that adorns the wearer’s upper limb like a viking arm ring. A viking arm ring that uses a Raspberry Pi Zero 2 W and a 4-inch screen to let those around you know that you’re listening to David Bowie.
Fancy a go at stop-motion animation? How about time-lapse photography? Or building a photo booth, or a nature cam, or even getting into face recognition using AI? You can do all this, and more, with Raspberry Pi and one of its range of camera modules. Rob Zwetsloot has been snapping away.
And that’s not all: we’ve hacked a toy robot arm to obey Micropython on a Raspberry Pi Pico, built a Raspberry Pi Pico drum machine, and blended the ancient art of origami with the much more recent innovation of an RGB LED, and loads more besides. Find out for yourself in the latest issue of Raspberry Pi Official Magazine, on sale now!
Yes, the title of this article sounds pretty crazy. But not only is it entirely possible through the lens of physics, but it is also practical to achieve in the real world using affordable parts. Jon Bumstead pulled it off with an Arduino, a photoresistor, and an inexpensive portable projector.
Today’s digital camera sensors are the result of a fairly linear progression from a camera obscura up through film cameras. The light from the scene enters through a lens that focuses all of that light on the 2D plane at the same time. The digital “sensor” is actually a whole grid of tiny sensors that each measure the light they receive. The camera records those values and reconstructing them gives you a digital image.
Bumstead’s “camera” works differently and only records a single point of light at a time. The entire camera is actually just an Arduino Mega 2560 (an UNO also works) with a photoresistor. The photoresistor provides a single analog light measurement and the Arduino reads that measurement, assigns a digital value, and passes the data to a PC.
Here’s the cool part: by only illuminating one point of the scene at a time, the camera can record each “pixel” in sequence. Those pixel values can then be reconstructed into an image. In this case, Bumstead used a portable video projector to provide the illumination. It scans the illumination point across the scene as the Arduino collects data from the photoresistor.
Bumstead also experimented with more complex techniques that rely on projected patterns and a lot of very fancy math to achieve similar results.
Finally, Bumstead showed that this also works when the photoresistor doesn’t have line-of-sight to the scene. In that demonstration, light from the scene bounces off a piece of paper, kind of like a mirror. The photodetector only sees the reflected light. But that doesn’t matter — remember, the photodetector is only seeing a single point of light anyway. Whether that light came directly from the surface of objects in the scene or bounced off paper first, the result is the same (just with a bit less quality, because the paper isn’t a perfect reflector).
From 1982 to 2000 Sony also made a line of pocket TVs, which didn’t catch on as much in the UK (who wants to walk around glued to a tiny portable screen, eh?). These devices, collectively called the Sony Watchman, came in many, many variants as screen technology evolved over 18 years of production. What’s […]
Are you an educator looking to make coding easier and faster to teach?
Join Andrea Richetta, Principal Product Evangelist at Arduino, and Roxana Escobedo, EDU Product Marketing Specialist, for a special Arduino Cloud Café live webinar on July 7th at 5PM CET.
You will discover how the new AI Assistant in Arduino Cloud can help you save valuable time in the classroom. We’ll also show you how the AI Assistant can generate, explain, and fix code, giving both you and your students the support you need to focus on creativity and learning.
What to expect
Watch live demos with the UNO R4 WiFi and Plug and Make Kit
Learn how to generate sketches, fix errors, and understand your code better
Get Andrea Richetta’s top 5 expert tips to work smarter with AI
Ask your questions live during our open Q&A
Whether you’re teaching STEM in a classroom or mentoring young developers, this session will help you engage with smarter, faster, AI-powered teaching.
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Don’t miss your chance to see the AI Assistant in action and find out how AI is shaping the future of Arduino development.
Young people everywhere deserve a high-quality computing education. But what a high-quality computing education looks like differs depending on a learner’s culture, context, and the existing provision in the country they live in. Therefore, adapting our educational resources for a range of contexts is a key part of our work at the Raspberry Pi Foundation, for example when we collaborate with partners to localise our Computing Curriculum resources.
In this blog post, we share our experiences of adapting curriculum resources with our partners in Kenya, and the impact of this work. This is the first post in a mini-series of three — look out for the upcoming ones about our partnerships in the Indian states of Odisha and Telangana.
Our computing curriculum partnerships in Kenya
Last year, we embarked on partnerships in two areas of Kenya and aimed to roll out computing curriculum resources to students in grades 4 to 9 in Kenyan schools:
During the 2024 academic year, we trained 39 local community trainers, who subsequently trained 453 teachers. We also adapted The Computing Curriculum materials to develop resources — lesson plans, presentation slides, and supporting activities — that are relevant and engaging in the schools our partners work with.
Impact in 2024
We estimate that around 55,000 students were reached by our Kenya computing curriculum resources in 2024. Most teachers who had used our resources felt the lessons had improved their students’ knowledge and skills. Of those who responded to our follow-up survey:
94% agreed that their students had improved their knowledge of computing concepts
92% agreed that their students have developed their computing skills
90% agreed that their students better understand how to use technology safely.
This was supported by conversations with teachers and students. In student focus groups, students were able to list topics they had learned about and skills they had developed.
“….The lessons have had a significant positive impact on the students. They now demonstrate greater confidence in using technology, particularly with tasks involving programming in Scratch. This has improved their problem-solving skills and made them more engaged in learning.” – Teacher, Mombasa
“In my computing lessons, I’ve learned how to use a computer safely and properly. I learned how to type, use the mouse, and open programs. We also learned about coding, which is really fun because we can make things happen on the screen by giving the computer instructions. I’ve also learned how to create a simple document using software like Excel sheets. I really enjoy using the computer to solve problems and make things work.” – Learner, Arid and Semi Arid Lands
Implementation: Challenges, solutions, and building on progress
While teachers tended to agree that students’ skills and knowledge had increased, fewer felt that most students had achieved the specific learning objectives identified in the resources. This was often due to the content being only partially delivered, for example, due to limited availability of computing equipment in schools. However, many students lacked prior experience with the topics covered in the lessons, suggesting a large improvement in their skills compared to a low baseline.
Similarly, some training sessions were affected by challenges with the equipment, infrastructure, and learning environment available. Teachers were appreciative of the training and many have begun to deliver the computing lessons, but often lacked prior experience with computing and hence requested additional support.
In response to feedback from partners and teachers, we made some updates to our Computing Curriculum and training resources in preparation for the 2025 academic year. For example, we increased the alignment to Kenya’s national curriculum, prepared a more comprehensive teacher guide, and incorporated time for teachers to discuss solutions to common delivery challenges during training.
In 2025, we are working with partners to upskill even more teachers and broaden the reach of our computing lessons to a further four counties. Our partners have now begun upskilling both new and existing teachers on the updated resources, and we will continue to work with them to monitor and evaluate their programme’s success in the coming months.
Want to learn more about our curriculum resources?
One reason that fans prefer mechanical keyboards over membrane alternatives is that mechanical key switches provide a very noticeable tactile sensation at the moment a key press registers. Whether consciously or not, users notice that and stop pressing the key all the way through the maximum travel — reducing strain and RSI potential. Developed by researchers at KAIST’s HCI Tech Lab, UltraBoard is a novel wearable that provides similar tactile feedback while typing in virtual reality.
UltraBoard’s designers wanted a device suitable for VR typing that would provide on-demand haptic feedback sensations, without complicated physical actuators. They achieved that with an array of ultrasonic transducers that produce strong soundwaves that the user can feel, but not hear. That array sits below the hand and can project localized soundwaves targeting specific points. So, typing the letter “A” on a virtual reality keyboard would cause the transducer array to blast soundwaves at the tip of the pinky finger.
An UltraBoard straps on to each of the user’s wrists and a servo motor near the strap tilts the transducer array to match the wrist angle, ensuring that the array is always directly underneath the user’s hand.
The prototype UltraBoard device uses both an Arduino Mega 2560 and an Arduino Micro board. They share duties, with the Micro controlling the servo motor and the transducer board, while the Mega controls an Ultraino driver board. They follow commands from a connected PC, which runs the virtual reality software that the user interacts with through a virtual reality headset.
The results of testing were mixed, but UltraBoard didn’t appear to provide a statistically significant improvement to typing speed. Even so, the concept is interesting and further testing may reveal other benefits, such as a more comfortable typing experience.
This year marked the 10th anniversary of Coolest Projects Belgium. The meticulously organised event was held in April by our partner CoderDojo Belgium, at Technopolis in Mechelen. Themed ‘On the move’, the event invited young creators to interpret movement however they liked – which they did in an impressive number of ways, creating projects ranging from mobile robots and Scratch animations to AI tools, health tech devices, and a musical drink maker.
With 52 inspiring creations showcased by 71 young people, there were too many awesome projects to list individually in this blog post. Here are just a few of our highlights from a day filled with big ideas and brilliant builds.
Rune | IINTS (Insulin Is Not the Solution)
Rune, who has type 1 diabetes, built his own open-source insulin pump powered by Raspberry Pi Pico W and featuring a custom motor-controlled delivery system designed in Autodesk Fusion. Rune’s pump calculates insulin doses based on carbohydrate amounts entered – all with the goal of empowering people, raising awareness, and making medical technology more accessible.
Amir | AmirAI
Amir might only be 10, but he is already experimenting with chatbots and AI in creative and playful ways. His self-coded AI assistant could respond live to visitors’ prompts, producing jokes and answers to questions. Amir’s project was a great demonstration of how accessible complex technologies can be when you give young people space to explore them.
Jules | Operatie Mocktail
This Arduino-powered machine blends mocktails based on your music choices. Pick a song, and the machine mixes a custom drink to match the song’s mood. It is a joyful combination of engineering, flavour science, and artistic flair. Jules described it best: “I want to create a unique drinking experience that connects taste and music in a surprising way.” We think it’s just right!
Mona | On the Move
Mona’s project is a reimagination of Michael Rosen’s poem On the Move created in Scratch, featuring animation, sound, and voice-over. It is a wonderful example of how digital storytelling can give new life to spoken word, and how creative coding platforms like Scratch provide space for emotion and expression.
Digital making: more than just a skill
Beyond these projects, the showcase included creations such as autonomous robots, arcade games, imaginative interface devices, and even a computer-controlled magic wand factory made of Lego bricks. Whether it was a creator’s very first Scratch project or a hand-built automaton, the range of work on display showed that coding and digital making are not just skills – they’re tools for self-expression, exploration, and change.
We would like to say a massive thank you to CoderDojo Belgium for hosting such an incredible event, and to the young creators, families, volunteers, and judges who made it such a success.
We are already looking forward to seeing what participants will create next!
At Code Club, we believe learning to code should be as fun as it is empowering — what better way to start than making your own game?
Whether it’s about pixelated pirates, racing robots, or a time-travelling llama, creating a game is one of the most exciting ways to explore coding. We’ve seen young people go from “I like Minecraft” to “I’ve built a space adventure with hedgehogs” in no time at all.
Why games?
Games are more than just fun. They’re also a brilliant gateway into problem solving, creativity, and computational thinking. When you create your own game, you learn how to:
Use logic and sequences to control what happens
Trigger events and interactions through code
Build characters, stories, and rules — and see them come to life
And perhaps most importantly, you get instant feedback. If your code doesn’t quite work, you’ll know right away, and you’ll have the chance to fix it, test it, and improve it.
Three fun game projects to try today
We’ve created a free library of step-by-step game projects that work in a browser, that require no previous experience, and that let imagination lead the way.
Here are three brilliant beginner-friendly projects to try at Code Club or at home:
Make: A fast-paced game where the player must dodge incoming objects
Try: Changing the game to set it in space, underwater, or inside a volcano
What does the community think?
Games are a great starting point because they’re naturally motivating: young people see what they’ve made work, and that’s incredibly empowering.
“I started with ‘Catch the dot’. I changed the sprite to a spaceship and then added power-ups and space cats. Now I’m making my own levels!” – Lila, 11, Creator
“When my son made his first Scratch game, it was the first time he explained what a loop was to me. Games build confidence.” – Laura, parent and Code Club Mentor
Build, remix, and level up
Once you’ve finished a project, the real fun begins. With just a few changes, creators can build their own levels or make the game more challenging, design custom characters and backdrops, or invent brand new rules and mechanics.
And if they want to share ideas and collaborate with others, joining a Code Club is the perfect next step.
Ready to get started?
You can find all the projects mentioned here — and many more — on the Code Club projects site. They’re free to use and designed to help creators grow in confidence as they learn to code.
Creating a computer program involves many different skills — knowing how to code is just one part. When we teach programming to young people, we want to guide them to learn these skills in a structured way. The ‘levels of abstraction’ framework is a great tool for doing that. This blog describes how using the framework will benefit you and your learners in the computing classroom.
Find practical tips for using the ‘levels of abstraction’ framework with your learners
Read a summary of the research behind the framework
Learning to program: Everything at once?
Creating a program from the ground up can be daunting, especially for new learners. Without support, they’ll likely get stuck sooner or later; programs rarely work the first time round. And the more complex the problem that a program is addressing, the more likely it is that the first version of the program won’t work.
One reason that learning to program can be challenging is that it involves understanding a lot of specific concepts and applying many varied skills. From early on in their learning journey, young people need to have a firm grasp of concepts such as repetition, selection, variables, and functions. Also fundamental to learning to program well is the skill of abstraction: understanding a task and identifying which details are relevant and which can be ignored.
To get to grips with all these different concepts and skills, young people need structure — otherwise they’ll try to hold everything in their head at once, and likely feel overwhelmed by the cognitive load. This sort of experience may cause them to disengage instead of persisting. They may even decide that programming is not for them.
In light of these challenges, the ‘levels of abstraction’ framework is a great tool for teaching.
The benefits of the ‘levels of abstraction’ framework
The framework breaks programming down into four levels, each focusing on a different aspect of creating a program:
Problem: Analysing the problem or task the program should address, to understand and record the requirements.
Design: Turning the analysis into an algorithm — a set of steps for the computer to follow to create the desired output. This can involve flowcharts or storyboards, but importantly no code.
Code: Developing the code based on the design (and building the physical components if any are involved).
Running the code: Testing the code, checking outputs, and debugging where necessary.
Throughout the processes of developing a program, learners (and professional programmers) move between these levels as they implement their designs and debug them, sometimes even returning to the problem level if more analysis or clarification is needed.
Potential benefits of the ‘levels of abstraction’ framework for teachers:
It helps you break down the activity of programming into discrete parts.
It helps you engage your learners, as you can show them that programming involves more than knowing how to code.
If your learners get stuck with their programming, the framework can help you guide them to a solution.
Potential benefits for learners:
The framework will help them think through all the steps needed to create a program that works, and practise their problem-solving skills and analytical thinking.
They will more readily see how programming connects to their world — at the problem level — and find aspects of programming where they have strengths and can use their creativity.
They will gain a stronger idea of how software is built in the tech sector.
Our new Quick Read shares tips on how to best use the framework in your teaching.
Things to aim for when using the framework with your learners:
Be aware of what level they are working at and when it’s time to switch to a different one.
Understand that, when they encounter an issue with their program, they can step back and use the framework to figure out where the issue comes from. The issue might be a bug in the code, the algorithm not working as intended, or a description of the problem not taking into account something important.
We hope you find the framework useful. If you have ideas for how to use it in your teaching, why not share them in the comments?
Teaching programming: The wider context
When following the ‘levels of abstraction’ approach, learners need to explain how programs work and debug them. That means program comprehension is a key skill here. You may have already helped your learners to develop and practise this skill, for example with the PRIMM approach. The Block Model is another useful tool for helping your learners talk about various aspects of a program. And if you use the pair programming approach in programming activities, your learners can improve their program comprehension by talking about their code with each other. On our website, you’ll find more guidance on the best ways to teach programming and computing.
And what about generative artificial intelligence (AI) tools for programmers? In the age of AI, we think young people still need to learn to code because it empowers them to navigate and think critically about all digital technologies, including AI. And while generative AI tools can help a skilled programmer create quality code more quickly, more research is needed to show whether such tools help school-age young people build their understanding as they learn to code. You can see some of the great work being done in this area if you catch up with our 2024 research seminar series.
The ‘levels of abstraction’ framework is useful in your teaching no matter what tools young people use to create programs. Even with an AI tool, they will still need to work at all four levels of abstraction to program effectively.
If you ask someone to think of a battery, they’re probably going to picture a chemical battery, like a AA alkaline or a rechargeable lithium-ion battery. But there are other kinds of batteries that store energy without any fancy chemistry at all. If you find a way to save energy for later, you have a useful battery. Erik, of the Concept Crafted Creations YouTube channel, achieved that by storing kinetic energy in a spinning flywheel weighted with water.
This isn’t a crazy idea, because flywheels exist specifically to store kinetic energy in a spinning mass. In this case, most of that mass comes from tubes full of water. Water is cheaper than something like cast iron and it is easy to adjust the levels to maintain perfect balance.
But this wet flywheel has another trick up its sleeve: adjustable moment of inertia. Watch an ice skater as they tuck into spin and you’ll understand this. By pulling their arms and legs close their axis of rotation, the skater can reduce their overall moment of inertia and increase their speed. Erik’s flywheel can do the same thing by actuating the cylinders of water to bring them in closer to the rotational axis.
To control that process, Erik used an Arduino Nano board housed in a simple laser-cut box with a potentiometer for adjusting speed, and buttons to control power and the arm actuation. A beefy brushless DC motor spins up the flywheel under power. Then, when it is time to collect that power (such as to power the lightbulb Erik used for demonstration), that motor acts as a dynamo, like in a generator.
As a battery for long-term power storage, this isn’t very practical. In a vacuum with perfect frictionless bearings, it would be. But in the real-world the flywheel will slow down on its own in short order. Even so, it is still a great illustration of the concept.
We are proud to announce two groundbreaking additions to the Arduino Pro portfolio: the Arduino Stella and Portenta UWB Shield, developed in partnership with Truesense. These advanced tools leverage ultra-wideband (UWB) technology to redefine precision tracking, indoor navigation, and contactless human-machine interactions, empowering IoT innovation across industries. Whatever you have in mind, you’ll leverage streamlined development thanks to ready-to-use Arduino IDE libraries, examples, and tutorials, enabling you to move from concept to prototype faster.
With UWB technology, you can achieve pinpoint accuracy in even the most complex environments, connect effortlessly with UWB-enabled smartphones and cloud platforms, and ensure your data remains private and secure thanks to UWB’s hard-to-intercept signals. You can learn more about our collaboration with Truesense and the power of UWB technology in our recent blog post: Arduino and Truesense partner to bring UWB technology to millions.
Arduino Stella shines for precision and versatility
Featuring an nRF52840 microcontroller and Truesense DCU040 module, the Arduino Stella delivers unparalleled accuracy for real-time tracking. Its compact design and seamless integration with UWB-enabled smartphones and apps like NXP Trimension, Apple’s Nearby Interaction, and Android’s UWB Jetpack library make it the perfect solution for modern tracking and automation needs.
Stella excels in industries such as healthcare, logistics, and smart buildings, offering advanced functionality like:
Pinpointing location tracking for high-value assets
Intuitive human-machine interaction
Automated safety and monitoring systems
Reliable indoor navigation
Portenta UWB Shield extends the end-to-end capabilities of the Portenta family
Powered by the Truesense DCU150, the Portenta UWB Shield easily adds UWB connectivity to the Portenta C33. This versatile shield acts as a base station and a client device, enabling precise real-time location services (RTLS) and two-way ranging.
With its modular and robust design, the Portenta UWB Shield is ideal for:
Smart logistics with dynamic route optimization
Interactive environments for enhanced user experiences
Secure and responsive IoT systems
Expand possibilities with ultra-wideband!
Every new addition to our ecosystem is a tool designed to make innovation accessible and scalable for professionals across industries. The Arduino Stella and Portenta UWB Shield, in particular, make it easier than ever to tackle applications such as:
Human-machine interaction: Enable intuitive commands and real-time feedback using UWB-equipped devices.
Follow-me AGVs: Automate logistics with autonomous vehicles that dynamically follow workers in warehouses.
Secure item transportation: Track critical items with proximity alerts and temperature monitoring during transit, leveraging compatibility with Modulino nodes.
Residential access control: Automate door access for authorized personnel with UWB-enabled smartphones.
EV automatic recharge: Streamline EV charging by triggering the process based on real-time vehicle positioning.
High-value asset tracking: Monitor valuable equipment in real time with location alerts and optimization tools.
Ready to elevate your IoT projects to new heights, with unmatched precision, seamless integration, and secure communication? Find the Arduino Stella and Portenta UWB Shield on the Arduino Store today!
This blog post is written by Victor Murithi, Communications and Media Consultant at Young Scientists Kenya, one of our global partners for Experience AI in Kenya.
When over 100 teachers from across Kenya gathered at Kangaru High School in Embu County for the Kenya Science and Engineering Fair Nationals in April, few anticipated just how transformative a two-day workshop could be. Delivered by the Experience AI Young Scientists Kenya (YSK) team, with support from the Raspberry Pi Foundation, the training sparked more than curiosity — it sparked a shift in mindset.
This wasn’t just about introducing new tools: it was about empowering teachers to confidently lead their students into an artificial intelligence (AI)-driven future.
National reach and local impact
What began as a plan to train just 40 teachers quickly grew into something much bigger. By the time the workshop kicked off, 104 teachers from over 80 schools across 37 counties in Kenya, had registered and participated — nearly tripling the initial target.
This overwhelming interest confirmed a powerful insight: teachers are eager to understand AI, not only to better prepare their students for the future, but also for their own professional growth.
The workshop’s curriculum didn’t just focus on technical skills, it aimed to create confidence, clarity, and community among the attendees — key ingredients for successfully integrating AI into teaching and learning.
“Helping teachers move past their fear of AI and understand its potential is incredibly powerful. Because AI is the future, and through this training, we’re reaching the minds that will shape it,” explained Lucy Mwaniki, AI Community Trainer at YSK.
Practical skills, real outcomes
As part of the training, the attendees completed interactive worksheets, tested basic machine learning models, and sat a final comprehension test, something they found both validating and motivational.
“We were able to do the summative test… which turned out to be a very effective way of us understanding how in-depth and how well they grasped the knowledge,” says Lucy Mwaniki.
In one standout session, teachers collaboratively brainstormed ways AI could address national educational challenges. Ideas included models to assist students in selecting academic pathways within Kenya’s Competency Based Curriculum (CBC). Several teachers also successfully built working models, demonstrating the potential of applied learning.
“It was a very eye-opening session… some of the teachers were able to create a very basic model, which was a wonderful experience for them,” Lucy Mwanikiexplains.
What made this training exceptional was its immediate applicability and long-term vision. By the end of two days, teachers weren’t just AI-aware — they were AI-ready, with many already starting to explore how AI tools could support entrepreneurship, lesson planning, and personalised learning pathways.
Celebrating our achievements and impact
At the close of the training, each teacher received a Certificate of Participation, recognising their commitment to professional development and their new capacity to bring AI into the classroom. The awarding of certificates added a sense of accomplishment and pride, reinforcing that teachers are key drivers of technological transformation in education.
And the impact of the training was measurable:
95% of teachers agreed that the training increased their knowledge and confidence to teach AI concepts
88% of teachers agreed that the training was high quality and useful for preparing them to teach the Experience AI lessons
But it doesn’t end there, as Vanessa Inziani, Head of Programs at YSK, explains, “Our commitment doesn’t end with the training — we continue to support educators with resources, mentorship, and follow-up to ensure success in delivering the program in the classroom.”
Looking ahead towards a promising AI journey
With the rapidly evolving digital landscape, AI is no longer a distant concept — it’s a present-day classroom necessity. Yet, introducing AI into schools isn’t just about technical literacy; it’s about confidence, clarity, and community and the approach the Young Scientist Kenya team and Experience AI delivered during the two-day training is anchored in this belief.
As AI continues to shape the global education landscape, programs like Experience AI provide the bridge needed to equip teachers, inspire students, and future-proof education systems. The Kangaru High School session was not a one-off — it was a catalyst for systemic change.
Experience AI is scaling. As it expands across Kenya and beyond, the benefits are clear:
Empowered educators who gain confidence and skills to integrate AI in their teaching
Future-ready students who grasp foundational AI concepts and their real-world applications
Sustainable impact as trained teachers go on to influence thousands of learners in their communities
The journey from fear to fluency starts with a single step, a willingness for us all to explore what’s possible. Together, we can equip educators, inspire students, and shape Kenya’s future, one AI-literate classroom at a time.
About Experience AI
Experience AI is an AI literacy programme, co-developed by the Raspberry Pi Foundation and Google DeepMind, that teaches students aged 11 to 14 about AI and machine learning. Thanks to funding from Google.org, Young Scientists Kenya has partnered with the Raspberry Pi Foundation to provide free training to Kenyan educators, equipping them with the skills they need to effectively deliver the programme in their settings. They are one of two global partners working with the Raspberry Pi Foundation in Kenya.
Robot arms are very cool and can be quite useful, but they also tend to be expensive. That isn’t just markup either, because the components themselves are pricey. However, you can save a lot of money if you make some sacrifices and build everything yourself. In that case, you can follow Ruben Sanchez’s tutorial to create your own four degrees of freedom robot arm from scratch.
This design has four actuated axes: the base, the shoulder, the elbow, and the wrist. Depending on the end effector you need, a gripper might count as another. It has a reach of up to 80cm and a maximum payload capacity of 350g, which is enough to move small objects.
Sanchez reduced the cost of this robot arm (compared to typical designs) in two ways. The first is by constructing the frame from aluminum sheet cut by hand, with laser markings as a guide template. The second is by using DC gear motors with external encoders for actuation, rather than purpose-built robotic actuators. They won’t have as much accuracy or repeatability, but they’re affordable.
An Arduino Due board controls the motors through Pololu drivers. The Arduino receives movement commands from a connected PC, which can look at the work area through an Intel RealSense camera attached by the end effector.
Sanchez provides the Arduino Sketch to get started, but encourages users to develop their own control software. To help with that, his writeup includes some nice explanations of inverse kinematics, the math involved, and how to implement it.
As data and data-driven technologies become a bigger part of everyday life, it’s more important than ever to make sure that young people are given the chance to learn data science concepts and skills.
David WeintropRotem Israel-FishelsonPeter F Moon
In our April research seminar, David Weintrop, Rotem Israel-Fishelson, and Peter Moon from the University of Maryland introduced API Can Code, a data science curriculum designed with high school students for high school students. Their talk explored how their innovative work uses real-world data and students’ own experiences and interests to create meaningful, authentic learning experiences in data science.
Quick note for educators: Are you interested in joining our free, exploratory data science education workshop for teachers on 10 July 2025 in Cambridge, UK? Then find out the details here.
David started by explaining the motivation behind the API Can Code project. The team’s goal was not to turn students into future data scientists, but to offer students the data literacy they need to explore and critically engage with a data-driven world.
The work was also guided by a shared view among leading teachers’ organisations that data science should be taught across all subjects in the K–12 curriculum. It also draws on strong research showing that when educational experiences connect with students’ own lives and interests, it leads to deeper engagement and better learning outcomes.
Reviewing the landscape
To prepare for the design of the curriculum, David, Rotem, and Peter wanted to understand what data science education options already exist for K–12 students. Rotem described how they compared four major K–12 data science curricula and examined different aspects, such as the topics they covered and the datasets they used. Their findings showed that many datasets were quite small in size, and that the datasets used were not always about topics that students were interested in.
The team also looked at 30 data science tools used across different K–12 platforms and analysed what each could do. They found that tools varied in how effective they were and that many lacked accessibility features to support students with diverse learning needs.
This analysis helped to refine the team’s objective: to create a data science curriculum that students find interesting and that is informed by their values and voices.
Participatory design
To work towards this goal, the team used a methodology called participatory design. This is an approach that actively involves the end users — in this case, high school students — in the design process. During several in-person sessions with 28 students aged 15 to 18 years old, the researchers facilitated low-tech, hands-on activities exploring the students’ identities and interests and how they think about data.
One activity, Empathy Map, involved students working together to create a persona representing a student in their school. They were asked to describe the persona’s daily life, interests, and concerns about technology and data:
The students’ involvement in the design process gave the team a better understanding of young people’s views and interests, which helped create the design of the API Can Code curriculum.
API Can Code: three units, three key tools
Peter provided an overview of the API Can Code curriculum. It follows a three-unit flow covering different concepts and tools in each unit:
Unit 1 introduces students to different types of data and data science terminology. The unit explores the role of data in the students’ daily lives, how use and misuse of data can affect them, different ways of collecting and presenting data, and how to evaluate databases for aspects such as size, recency, and trustworthiness. It also introduces them to RapidAPI, a hub that connects to a wide range of APIs from different providers, allowing students to access real-world data such as Zillow housing prices or Spotify music data.
Unit 2 covers the computing skills used in data science, including the use of programming tools to run efficient data science techniques. Students learn to use EduBlocks, a block-based programming environment where students can draw in JSON files from RapidAPI datasets, and process and filter data without needing a lot of text-based programming skills. The students also compare this approach with manual data processing, which they discover is very slow.
Unit 3 focuses on data analysis, visualisation, and interpretation. Students use CODAP, a web-based interactive data science tool, to calculate summary statistics, create graphs, and perform analyses. CODAP is a user-friendly but powerful platform, making it perfect for students to analyse and visualise their data sets. Students also practise interpreting pre-made graphs and the graphs and statistics that they are creating.
Peter described an example activity carried out by the students, showing how these three units flow together and build both technical skills and an understanding of the real-world uses of data science. Students were tasked with analysing a dataset from Zillow, a property website, to explore the question “How much does a house in my neighbourhood cost?” The images below show the process the students followed, which uses the data science skills and tools from all three units of the curriculum.
Click on an image to enlarge it.
Interest-driven learning in action
A central tenet of API Can Code is that students should explore data that matters to them. A diverse range of student interests was identified during the design work, and the curriculum uses these areas of interest, such as music, movies, sports, and animals, throughout the lessons.
The curriculum also features an open-ended final project, where students can choose a research question that is important to them and their lives, and answer it using data science skills.
The team shared two examples of memorable final projects. In one, a student set out to answer the question “Is Jhené Aiko a star?” The student found a publicly available dataset through an API provided by Deezer, a music streaming platform. She wrote a program that retrieved data on the artist’s longevity and collaborations, analysed the data, and concluded that Aiko is indeed a star. What stood out about this project wasn’t just the fact that the student independently defined stardom and answered their research question using real data, but that this was a truly personal, interest-driven project. David noted that the researchers could never have come up with this activity, since they had never previously heard of Jhené Aiko!
Jhené Aiko, an R&B singer-songwriter (Photo by Charito Yap, licensed under CC BY-ND 2.0)
Another student’s project analysed data about housing in Washington DC to answer the question “Which ward in DC has the most affordable houses?” Rotem explained that this student was motivated by her family thinking about moving away from the city. She wanted to use her project to persuade her parents to stay by identifying the most affordable ward in DC that they could move to. She was excited by the outcome of her project, and she presented her findings to other students and her parents.
These projects underscore the power of personally important data science projects driven by students’ interests. When students care about the questions they are exploring, they’re more invested in the process and more likely to keep using the skills and concepts they learn.
Resources
API Can Code is available online and completely free to use. Teachers can access lesson plans, tutorial videos, assessment rubrics, and more from the curriculum’s website https://apicancode.umd.edu/. The site also provides resources to support students, including example programs and glossaries.
Join our next seminar
In our current seminar series, we’re exploring teaching about AI and data science. Join us at our next seminar on Tuesday, 17 June from 17:00 to 18:30 BST to hear Netta Iivari (University of Oulu) introduce transformative agency and its importance for children’s computing education in the age of AI.
To sign up and take part in our research seminars, click below:
We’re heading to Milan! On July 2nd-4th, Arduino will be taking part in the EDGE AI FOUNDATION’s annual European event – a three-day gathering dedicated to exploring the future of artificial intelligence at the edge. With a mix of inspiring keynotes, hands-on workshops, product demos, and networking opportunities, this event brings together global leaders from academia and industry to shape what’s next in edge AI and tinyML.
Arduino is proud to be part of this community. You’ll find us on the exhibition floor with live demos of some of our most advanced edge computing solutions – from cloud-connected, AI-driven object recognition to intuitive device control via natural gestures, from robotics and environmental sensing to factory automation and beyond.
If you’re attending, don’t miss the talk by Arduino’s Chief Product Officer Marcello Majonchi on July 2nd at 10:25am – “Empowering at the Edge: the ‘Arduino way’ to AI” will unveil the next generation of tools designed to make the development of intelligent applications faster, easier, and more open than ever!
As always, we’re excited to show how powerful tools can still be open, accessible, and easy to use. By collaborating with organizations like the EDGE AI FOUNDATION, we’re helping more people explore AI at the edge and build real-world applications that are sustainable, scalable, and smart.
Curious to join us? The event is open to professionals, researchers, and students alike – and there’s discounted pricing for academic attendees. Head to the event site to register and check out the full program!
From 1982 to 2000 Sony also made a line of pocket TVs, which didn’t catch on as much in the UK (who wants to walk around glued to a tiny portable screen, eh?). These devices, collectively called the Sony Watchman, came in many, many variants as screen technology evolved over 18 years of production. What’s […]
We’re thrilled to celebrate yet another incredible year of young people reaching for the stars, as the European Astro Pi Challenge 2024/25 draws to a close. Teams from across Europe and ESA Member States are now receiving their well-deserved certificates and data from the International Space Station (ISS). It’s been a truly inspiring year, showcasing the phenomenal talent and dedication of young coders and scientists.
The European Astro Pi Challenge is an ESA Education project run in collaboration with us here at the Raspberry Pi Foundation. It offers young people the amazing opportunity to conduct scientific investigations in space by writing computer programs that run on Raspberry Pi computers on board the ISS, called Astro Pis.
There‘s a lot to celebrate from this year’s Astro Pi, so let’s take a look at some of the highlights for each of our inspiring Missions: Mission Zero and Mission Space Lab.
Figure 1: A selection of images taken by Mission Space Lab teams
Mission Zero: Inspiring coding, creativity, and inclusion
Mission Zero reached more young people than ever before in 2024/25, with 25,405 young people participating in 17,285 teams. After passing the rigorous testing and moderation processes, an amazing 17,109 teams (25,210 young people) were successful in getting their programs to run on the ISS.
One of the great things about Mission Zero is that we see a good gender balance in participation. This year, 44% of participants identified as “female” and 4% as “prefer to self-describe”, “prefer not to say”, or “other”. This means that Mission Zero has achieved a more balanced gender representation than is typically seen in computing subjects, where the ratio is around 20:80 girls to boys.
Mission Space Lab: More teams have their programs run in space
Mission Space Lab gives young people the opportunity to calculate the speed of the ISS in orbit using sensor and camera data collected from the Astro Pis on board the ISS. This year, 1859 young people in 552 teams participated in Mission Space Lab. Notably, 309 Mission Space Lab teams, or 95% of submissions, ran their programs on the ISS and are now analysing the data they collected. That’s 73 more teams achieving flight status than in 2023/24, and a total of 1084 young people receiving unique data sets from space and certificates.
Running a program in space is very different from testing it on the ground. It’s always interesting to see how well your program has performed and how accurate the final output is. Below, you can see a scatter graph of the team estimates produced by their programs. The actual speed of the ISS is no secret: it’s travelling about 7.67 kilometres per second. How have teams performed with the ISS speed task?
Figure 2: Mission Space Lab teams’ speed estimates graph
Inspiring and impactful
Another highlight from this year has been seeing how impactful participation can be for young people and mentors facilitating the activity. We receive lots of valuable feedback from the Astro Pi community each year, and it’s always heartwarming to hear what your experience has been and how we can improve the challenge. Here are a couple of quotes from the community who took part this year:
Mission Zero mentor: “Having their programs run in space really motivated them to take part because it was an exciting reward and something they wanted to talk about with their friends.”
Parent of a Mission Zero participant: “I was completely inexperienced in Python, but easily managed to help my 7-year-old.”
More Code Clubs participating in 2024/25
It has been great to see lots of Code Clubs taking part in Astro Pi this year, both for Mission Zero and Mission Space Lab. This year, 986 young people from 700 teams did Mission Zero at their Code Club: that’s double the number from 2023/24. Plus, 43 Mission Space Lab teams from Code Clubs took part. That’s 143 young people, or almost double the number compared to the year before.
We ran two code-alongs for the Code Club community this year, and it is encouraging to see increases for both missions. We will continue to support young people from all settings who want to take part in Astro Pi next year, whether it’s at school, Code Club, or other venues.
Conclusion
In summary, it’s been a great year for Astro Pi. We’ve reached lots of young people through the challenge, met many inspiring mentors, and seen some really positive trends. Plus, all the operations on the space station that make Astro Pi possible went smoothly: when you are running programs in space, that isn’t always the case!
None of it would have been possible without the tireless efforts of the teachers, mentors, and educators who help run Astro Pi in your communities. From everyone here at Mission Control, thank you.
If you’d like to tell us how we can provide more support to help you run Astro Pi, please email contact@astro-pi.org.
We’ll be back for more stellar space adventures in coding in September 2025.
Typically, consumer drones take off from the ground or some other solid surface. But that isn’t very cinematic and toss launches — when the pilot throws the drone up into the air — are a lot more interesting to watch. Sadly, NickFPV isn’t very good at tossing his drone and that invites ridicule in his videos’ comment sections. To redeem himself, he built this automatic drone launcher triggered by an Arduino.
When developing the launching mechanism, NickFPV found inspiration in his kitchen. Or more accurately, he found inspiration in the kitchens of cartoons, where toasters rocket charred bread to comical altitudes. He figured that if it works for toast, it could work for a micro drone. He just needed more stored kinetic energy.
As with a toaster, NickFPV’s mechanism stores kinetic energy in a spring. When released, that spring pulls up a platform riding on hardened steel rods. The spring and rods attach to a 3D-printed frame and a pin latch holds the platform in place until the launch. The drone sits on that platform and when the platform reaches the top, it stops while the drone continues skyward.
NickFPV could have tugged a string to pull out that pin, but the launcher is pretty small and that pin requires some force to pull. Doing that while standing safely a few feet away would inevitably drag the entire launcher. To solve that problem, NickFPV added an Arduino to trigger the launch.
That is an Arduino UNO R4 WiFi board and it controls a servo motor mounted on the launcher. At the press of a button, the servo yanks the string that pulls the latch pin. Power comes from a portable USB battery pack, so any location can become a launch pad.
The launcher proved to be a success and it throws the drone a good six feet up, where its motors can take over to achieve flight. Now, NickFPV’s viewers won’t see his poor throws.
We’re pleased to share highlights from the 2025 Code Club annual survey report today, showcasing another year of incredible achievements and the positive impact of the global Code Club community.
Code Club is a global movement of free coding clubs where school-aged young people — called creators — develop the confidence to create with digital technologies. Code Clubs take place in schools and community venues like youth clubs, libraries, and maker spaces and are run by teachers, educators, and volunteers from all walks of life — known as mentors. These incredible mentors make Code Clubs possible and we are so grateful for their hard work.
About the 2025 survey report
This Code Club annual survey report presents key responses from 775 mentors gathered via surveys and feedback from partners.
This year, 7,494 Code Clubs have confirmed they have been active in the last two years, with clubs in 102 countries. We estimate 257,000 creators are involved in clubs and 43% of creators are female. As one UK Code Club mentor put it: “Girls who didn’t think it was for them now have confidence”.
Code Clubs have a positive impact on young creators
In 2024, an independent evaluation by the Durham University Evidence Centre for Education provided evidence of positive outcomes for young people attending Code Clubs. We are continuing to build on this evidence, with 96% of mentors responding to our surveys agreeing that creators have increased skills in computing and digital making, as well as increased confidence to engage with technology as a result of attending a Code Club.
Here are a few of the examples mentors gave of the impact Code Club has on creators:
Confidence: “[Creators become] more confident using technology and making friends. Some really come out of their shell compared to when they started.” – Code Club mentor, UK
Skill development: “They come into the club with no coding skills (some barely know how to use a computer) and leave as competent, literate, coders.” – Code Club mentor, Canada
Enjoyment: “One of our core principles is that coding should be fun… we give them creative ways to expand on the task. They learn to push themselves a bit beyond a task, and look for more things.” – Code Club mentor, the Netherlands
Social skills: “One great outcome has been the socialization that occurs. Kids in our club are definitely making friendships and improving their soft skills.” – Code Club mentor, USA
Continued participation: “It has increased their passion for tech and how to create new things to solve problems.” – Code Club mentor, Ghana
Increasing access to technology
Code Club also plays an important role in increasing access to technology for creators who would otherwise not have access. We work with partners across the world to run clubs in areas of educational disadvantage to ensure that Code Clubs are available to creators from all backgrounds to address this need.
In some regions, Code Club provides creators with their first significant encounter with digital making. A mentor in Kenya told us that Code Club ensured that creators in his area were not “left behind”. A Code Club mentor in Tunisia told us “[…] access to coding is very limited, our club contributes to reducing this inequality”.
Next steps
Read the full report to dive deeper into the data and stories from the Code Club community!
We are an impact-focused organisation and are always looking to understand how we can improve and increase the impact we have on the lives of children and young people. Over the coming weeks we will be reviewing the feedback we have received to understand how we can support the Code Club community even better.
Are you a teacher who is interested in data science education for key stage 5 (age 16 to 18)? Then we invite you to join our free, in-person workshop exploring the topic, taking place in Cambridge, UK on 10 July 2025.
You will be among the very first educators to see some of our first test activities for teacher training to build data science concepts, and your contributions will feed into our future work. Sign up by 20 June to take part.
Data science: What do we need to teach school-age learners?
Current artificial intelligence (AI) methods, especially machine learning (ML), rely heavily on data. While young people learn mathematics, and some statistics, at school, data science concepts are not commonly taught.
To complement our work on AI literacy, we have been investigating what data science teaching resources and education research are currently available.
Our goals for this work are:
To figure out what data science concepts may need to be taught in schools, initially with a focus on key stage 5
To develop related teacher professional development and classroom resources
Join us to discuss data science education
If you are interested in data science education for young people, and maybe even have experience of teaching it to learners aged 16 to 18 in your school (in any subject, including computer science, social sciences, mathematics, statistics, and ethics), please join our free workshop on Thursday 10 July in our office in Cambridge. We are able to reimburse some travel expenses.
At the workshop:
We would love to hear about your experience of teaching any elements of data science
We will share some exploratory concept building activities with you and discuss them together
You’ll be the first group of working teachers we will share these activities with — your feedback will be invaluable, and you’ll have the chance to shape our work going forward.
You will then receive more information from us by 27 June. Spaces in the workshop are limited, so please do not book any travel until we confirm your space.
We’re looking forward to shaping the future of data science education with you.
If you like to listen to those “deep focus” soundtracks that are all ambient and relaxing, then you’ve heard a tongue drum in action. A tongue drum, or tank drum, is a unique percussion instrument traditionally made from an empty propane cylinder — though purpose-built models are now common. Several tongues are cut into one end cap and weighted to produce specific notes when struck. As with all instruments, playing a tongue drum is an art. To simplify that, Jeremy Cook built a robot capable of playing a small tongue drum.
When robotizing a percussion instrument, it is common to use solenoids and that is what Cook did here. Solenoid actuators like these move linearly and can strike with pretty decent force, which makes them a good choice. Cook’s drum has eight tongues, so his robot has eight solenoids held by flexible friction arms mounted onto a C-shaped laser-cut MDF frame. PVC pipes actual as the vertical structural supports on that frame.
To tell the robot what tunes to play, Cook added a MIDI input that comes through an Opta-compatible I2C and serial adapter of his own design. That adapter is available for sale on Tindie if you want one.
The MIDI input can come from a something like a keyboard for real-time manual control, or it can come from a PC for playing pre-written (or algorithm-generated) ambient hits. If you attended the Orlando Maker Faire last year, you may have had a chance to try this robotic tongue drummer for yourself.
Drone flight controllers do so much more than simply receive signals and tell the drone which way to move. They’re responsible for constantly tweaking the motor speeds in order to maintain stable flight, even with shifting winds and other unpredictable factors. For that reason, most flight controllers are purpose-built for the job. But element14’s Milos Rasic was building his own drone from scratch and found that the Arduino Nicla Vision board makes a great flight controller.
To perform that critical job of keeping the drone stable, the flight controller needs precises information about the orientation of the drone and any movement in three-dimensional space. Luckily, the Nicla Vision has an integrated six-axis motion sensor that is perfect for the job. It has also a powerful STM32H7 microcontroller, a built-in camera for machine vision and learning tasks, onboard Wi-Fi and Bluetooth connectivity, and more. And because it is very small (22.86×22.86mm) and very light, it is a good choice for a drone.
Rasic designed and made the entire drone from zero, using 8520 brushed DC motors and a 3D-printed frame. That is cool, but it isn’t uncommon. The Nicla Vision-based flight controller is what stands out the most.
Rasic developed a custom PCB for the Nicla Vision that acts like a breakout board and contains a few other useful components, such as for regulating and boosting power. But it didn’t need much, as the Nicla Vision already has most of the necessary hardware.
While he could have turned to existing flight controller firmware, Rasic chose to develop his own and that is the most impressive part of this project. That necessitated the creation of three PID (proportional-integral-derivative) controller algorithms for balancing pitch, roll, and yaw. Those work with control inputs to let the drone hover and move stably. The control signals come from a PC over Wi-Fi, with the pilot providing input through a USB flight stick.
The drone isn’t yet flying well, as PID tuning is a challenge for even the most experienced drone builders. But the foundation is there for Rasic to build on.
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